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Computer Science > Learning

Title:
DOOM Level Generation using Generative Adversarial Networks

Abstract: We applied Generative Adversarial Networks (GANs) to learn a model of DOOM
levels from human-designed content. Initially, we analysed the levels and
extracted several topological features. Then, for each level, we extracted a
set of images identifying the occupied area, the height map, the walls, and the
position of game objects. We trained two GANs: one using plain level images,
one using both the images and some of the features extracted during the
preliminary analysis. We used the two networks to generate new levels and
compared the results to assess whether the network trained using also the
topological features could generate levels more similar to human-designed ones.
Our results show that GANs can capture intrinsic structure of DOOM levels and
appears to be a promising approach to level generation in first person shooter
games.